Rating difficulty of questions

ABSTRACT

A mechanism is provided in a data processing system for rating difficulty of a question. The mechanism receives an input question and generates one or more candidate answers from a corpus of knowledge using a pipeline of software engines. The pipeline of software engines generates a plurality of features extracted from the question, the one or more candidate answers, or the corpus of knowledge. The mechanism then generates a question difficulty score based on the plurality of features using a machine learning model. The machine learning model maps features to assigned weights for scaling the difficulty score.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for ratingdifficulty of questions.

With the increased usage of computing networks, such as the Internet,humans are currently inundated and overwhelmed with the amount ofinformation available to them from various structured and unstructuredsources. However, information gaps abound as users try to piece togetherwhat they can find that they believe to be relevant during searches forinformation on various subjects. To assist with such searches, recentresearch has been directed to generating Question and Answer (QA)systems, which may take an input question, analyze it, and returnresults indicative of the most probable answer to the input question. QAsystems provide automated mechanisms for searching through large sets ofsources of content, e.g., electronic documents, and analyze them withregard to an input question to determine an answer to the question and aconfidence measure as to how accurate an answer is for answering theinput question.

One such QA system is the Watson™ system available from InternationalBusiness Machines (IBM) Corporation of Armonk, N.Y. The Watson™ systemis an application of advanced natural language processing, informationretrieval, knowledge representation and reasoning, and machine learningtechnologies to the field of question answering. The Watson™ system isbuilt on IBM's DeepQA™ technology used for hypothesis generation,massive evidence gathering, analysis, and scoring. DeepQA™ takes aninput question, analyzes it, decomposes the question into constituentparts, generates one or more hypotheses based on the decomposed questionand results of a primary search of answer sources, performs hypothesisand evidence scoring based on a retrieval of evidence from evidencesources, performs synthesis of the one or more hypotheses, and based ontrained models, performs a final merging and ranking to output an answerto the input question along with a confidence measure.

Various United States patent application Publications describe varioustypes of question and answer systems. U.S. Patent ApplicationPublication No. 2011/0125734 discloses a mechanism for generatingquestion and answer pairs based on a corpus of data. The system startswith a set of questions and then analyzes the set of content to extractanswer to those questions. U.S. Patent Application Publication No.2011/0066587 discloses a mechanism for converting a report of analyzedinformation into a collection of questions and determining whetheranswers for the collection of questions are answered or refuted from theinformation set. The results data are incorporated into an updatedinformation model.

SUMMARY

In one illustrative embodiment, a method, in a data processing system,is provided for rating difficulty of a question. The method comprisesreceiving an input question and generating one or more candidate answersfrom a corpus of knowledge using a pipeline of software engines. Thepipeline of software engines generates a plurality of features extractedfrom the question, the one or more candidate answers, or the corpus ofknowledge. The method further comprises generating a question difficultyscore based on the plurality of features using a machine learning model.The machine learning model maps features to assigned weights for scalingthe difficulty score.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented;

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment;

FIG. 4 is a block diagram illustrating training of a system for ratingdifficulty in accordance with an illustrative embodiment;

FIG. 5 is a block diagram illustrating a system for rating difficulty inaccordance with an illustrative embodiment;

FIG. 6 is a block diagram of a system for selecting a question set inaccordance with an illustrative embodiment;

FIG. 7 is a flowchart illustrating operation of training a system forrating difficulty in accordance with an illustrative embodiment;

FIG. 8 is a flowchart illustrating operation of a system for ratingdifficulty in accordance with an illustrative embodiment; and

FIG. 9 is a flowchart illustrating operation of a system for selecting aquestion set in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments provide a mechanism for rating difficultyof questions. In many cases, it is desirable to have an estimate ofquestion difficulty. For example, one may use a large set of quizquestions to test students' knowledge. It is desirable to expose eachstudent to questions of comparable difficulty from a comparable domain.A question generation system may generate factual questions that requireknowledge of multiple facts from a corpus of knowledge, synthesis ofmaterial, making an inferential leap, or applying a concept in a novelway. Some facts may be less known than other facts. As a result, thequestion using the less known fact will generally be harder to answer.Such a question generation system may use the target measure of questioncomplexity to calibrate which questions are actually generated.

An illustrative embodiment provides a mechanism to rate difficulty of afactual question given a corpus of knowledge. The corpus is in astructured format, an unstructured format, or a combination ofstructured and unstructured. One may use the difficulty rating toestimate the difficulty of a question posed regarding the corpus ofknowledge within a computer-based instruction scenario. One may use thedifficulty rating to generate questions with a desired target difficultylevel. For example, one may generate quiz sets and to train a naturallanguage processing (NLP) system, such as a question answering (QA)system.

In one embodiment, the system evaluates a set of questions and assigns adifficulty score. The system then uses the set of questions to generatesubsequent question sets, quizzes, and the like. In another embodiment,the question generating system has some question difficulty-relatedpolicies. Such a system may, for example, discard a question candidateif it does not meet the expected difficulty. The system may beconfigured to compose quizzes with a specified mixture of difficultylevels of the questions, resulting in a set of unique quizzes havingequivalent overall difficulty level.

An advantage of assigning difficulty levels through automated methods,as opposed to manual assignment, is that the difficulty score may varyper domain, or within one domain, when the corpus of information used asa source of support changes. Therefore, the same question may have adifferent difficulty in a Social Sciences domain than in an Arts domain,or when created for medical students versus undergraduates.

A “mechanism,” as used herein, may be an implementation of the functionsor aspects of the illustrative embodiments in the form of an apparatus,a procedure, or a computer program product. The mechanisms describedherein may be implemented as specialized hardware, software executing ongeneral purpose hardware, software instructions stored on a medium suchthat the instructions are readily executable by specialized or generalpurpose hardware, a procedure or method for executing the functions, ora combination of the above.

The above aspects and advantages of the illustrative embodiments of thepresent invention will be described in greater detail hereafter withreference to the accompanying figures. It should be appreciated that thefigures are only intended to be illustrative of exemplary embodiments ofthe present invention. The present invention may encompass aspects,embodiments, and modifications to the depicted exemplary embodiments notexplicitly shown in the figures but would be readily apparent to thoseof ordinary skill in the art in view of the present description of theillustrative embodiments.

FIGS. 1-3 are directed to describing an example Question/Answer,Question and Answer, or Question Answering (QA) system, methodology, andcomputer program product with which the mechanisms of the illustrativeembodiments may be implemented. As will be discussed in greater detailhereafter, the illustrative embodiments may be integrated in, and mayaugment and extend the functionality of, these QA mechanisms with regardto automatically generating testing/training questions and answers byperforming pattern based analysis and natural language processingtechniques on the given corpus for quick domain adaptation.

Thus, it is important to first have an understanding of how question andanswer creation in a QA system may be implemented before describing howthe mechanisms of the illustrative embodiments are integrated in andaugment such QA systems. It should be appreciated that the QA mechanismsdescribed in FIGS. 1-3 are only examples and are not intended to stateor imply any limitation with regard to the type of QA mechanisms withwhich the illustrative embodiments may be implemented. Manymodifications to the example QA system shown in FIGS. 1-3 may beimplemented in various embodiments of the present invention withoutdeparting from the spirit and scope of the present invention.

QA mechanisms operate by accessing information from a corpus of data orinformation (also referred to as a corpus of content), analyzing it, andthen generating answer results based on the analysis of this data.Accessing information from a corpus of data typically includes: adatabase query that answers questions about what is in a collection ofstructured records, and a search that delivers a collection of documentlinks in response to a query against a collection of unstructured data(text, markup language, etc.). Conventional question answering systemsare capable of generating answers based on the corpus of data and theinput question, verifying answers to a collection of questions for thecorpus of data, correcting errors in digital text using a corpus ofdata, and selecting answers to questions from a pool of potentialanswers, i.e., candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsmay know what questions the content is intended to answer in aparticular topic addressed by the content. The content may also answerother questions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA system. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA system to identify thesequestion-and-answer attributes of the content.

Operating on such content, the QA system generates answers for inputquestions using a plurality of intensive analysis mechanisms, whichevaluate the content to identify the most probable answers, i.e.,candidate answers, for the input question. The illustrative embodimentsleverage the work already done by the QA system to automaticallygenerate testing/training questions and answers by performing patternbased analysis and natural language processing techniques on the givencorpus for quick domain adaptation.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of aquestion/answer creation (QA) system 100 in a computer network 102. Oneexample of a question/answer generation which may be used in conjunctionwith the principles described herein is described in U.S. PatentApplication Publication No. 2011/0125734, which is herein incorporatedby reference in its entirety. The QA system 100 may be implemented onone or more computing devices 104 (comprising one or more processors andone or more memories, and potentially any other computing deviceelements generally known in the art including buses, storage devices,communication interfaces, and the like) connected to the computernetwork 102. The network 102 includes multiple computing devices 104 incommunication with each other and with other devices or components viaone or more wired and/or wireless data communication links, where eachcommunication link comprises one or more of wires, routers, switches,transmitters, receivers, or the like. The QA system 100 and network 102enable question/answer (QA) generation functionality for one or more QAsystem users via their respective computing devices 110, 112. Otherembodiments of the QA system 100 may be used with components, systems,subsystems, and/or devices other than those that are depicted herein.

The QA system 100 may be configured to implement a QA system pipeline108 that receive inputs from various sources. For example, the QA system100 may receive input from the network 102, a corpus of electronicdocuments 106, QA system users, or other data and other possible sourcesof input. In one embodiment, some or all of the inputs to the QA system100 is routed through the network 102. The various computing devices 104on the network 102 include access points for content creators and QAsystem users. Some of the computing devices 104 include devices for adatabase storing the corpus of data 106 (which is shown as a separateentity in FIG. 1 for illustrative purposes only). Portions of the corpusof data 106 may also be provided on one or more other network attachedstorage devices, in one or more databases, or other computing devicesnot explicitly shown in FIG. 1. The network 102 includes local networkconnections and remote connections in various embodiments, such that theQA system 100 may operate in environments of any size, including localand global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus of data 106 for use as part of a corpus of data with the QAsystem 100. The document includes any file, text, article, or source ofdata for use in the QA system 100. QA system users access the QA system100 via a network connection or an Internet connection to the network102, and input questions to the QA system 100 to be answered by thecontent in the corpus of data 106. In one embodiment, the questions areformed using natural language. The QA system 100 interprets the questionand provide a response to the QA system user, e.g., QA system user 110,containing one or more answers to the question. In some embodiments, theQA system 100 provides a response to users in a ranked list of candidateanswers.

The QA system 100 implements a QA system pipeline 108, which comprises aplurality of stages for processing an input question, the corpus of data106, and generating answers for the input question based on theprocessing of the corpus of data 106. The QA system pipeline 108 will bedescribed in greater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the QA system 100 may be the Watson™QA system available from International Business Machines Corporation ofArmonk, N.Y., which is augmented with the mechanisms of the illustrativeembodiments described hereafter. The Watson™ QA system receives an inputquestion, which it then parses to extract the major features of thequestion, which in turn are then used to formulate queries that areapplied to the corpus of data. Based on the application of the queriesto the corpus of data, a set of hypotheses, or candidate answers to theinput question, are generated by looking across the corpus of data forportions of the corpus of data that have some potential for containing avaluable response to the input question.

The Watson™ QA system then performs deep analysis on the language of theinput question and the language used in each of the portions of thecorpus of data found during the application of the queries using avariety of reasoning algorithms. There may be hundreds or even thousandsof reasoning algorithms applied, each of which performs differentanalysis, e.g., comparisons, and generates a score. For example, somereasoning algorithms look at the matching of terms and synonyms withinthe language of the input question and the found portions of the corpusof data. Other reasoning algorithms look at temporal or spatial featuresin the language, while others evaluate the source of the portion of thecorpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the Watson™ QA system. Thestatistical model is then used to summarize a level of confidence thatthe Watson™ QA system has regarding the evidence that the potentialresponse, i.e., candidate answer, is inferred by the question. Thisprocess is repeated for each of the candidate answers until the Watson™QA system identifies candidate answers that surface as beingsignificantly stronger than others and thus, generates a final answer,or ranked set of answers, for the input question. More information aboutthe Watson™ QA system may be obtained, for example, from the IBMCorporation website, IBM Redbooks, and the like. For example,information about the Watson™ QA system can be found in Yuan et al.,“Watson and Healthcare,” IBM developerWorks, 2011 and “The Era ofCognitive Systems: An Inside Look at IBM Watson and How it Works” by RobHigh, IBM Redbooks, 2012.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments may be implemented. Dataprocessing system 200 is an example of a computer, such as server 104 orclient 110 in FIG. 1, in which computer usable code or instructionsimplementing the processes for illustrative embodiments of the presentinvention may be located. In one illustrative embodiment, FIG. 2represents a server computing device, such as a server 104, which, whichimplements a QA system 100 and QA system pipeline 108 augmented toinclude the additional mechanisms of the illustrative embodimentsdescribed hereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)202 and south bridge and input/output (I/O) controller hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices include, for example, Ethernet adapters, add-incards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and may be loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention may be performed by processing unit 206 using computerusable program code, which may be located in a memory such as, forexample, main memory 208, ROM 224, or in one or more peripheral devices226 and 230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG.2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 illustrates a QA system pipeline for processing an input questionin accordance with one illustrative embodiment. The QA system pipelineof FIG. 3 may be implemented, for example, as QA system pipeline 108 ofQA system 100 in FIG. 1. It should be appreciated that the stages of theQA system pipeline shown in FIG. 3 may be implemented as one or moresoftware engines, components, or the like, which are configured withlogic for implementing the functionality attributed to the particularstage. Each stage may be implemented using one or more of such softwareengines, components or the like. The software engines, components, etc.may be executed on one or more processors of one or more data processingsystems or devices and may utilize or operate on data stored in one ormore data storage devices, memories, or the like, on one or more of thedata processing systems. The QA system pipeline of FIG. 3 may beaugmented, for example, in one or more of the stages to implement theimproved mechanism of the illustrative embodiments described hereafter,additional stages may be provided to implement the improved mechanism,or separate logic from the pipeline 300 may be provided for interfacingwith the pipeline 300 and implementing the improved functionality andoperations of the illustrative embodiments.

As shown in FIG. 3, the QA system pipeline 300 comprises a plurality ofstages 310-380 through which the QA system operates to analyze an inputquestion and generate a final response. In an initial question inputstage 310, the QA system receives an input question that is presented ina natural language format. That is, a user may input, via a userinterface, an input question for which the user wishes to obtain ananswer, e.g., “Who are Washington's closest advisors?” In response toreceiving the input question, the next stage of the QA system pipeline300, i.e. the question and topic analysis stage 320, parses the inputquestion using natural language processing (NLP) techniques to extractmajor features from the input question, classify the major featuresaccording to types, e.g., names, dates, or any of a plethora of otherdefined topics. For example, in the example question above, the term“who” may be associated with a topic for “persons” indicating that theidentity of a person is being sought, “Washington” may be identified asa proper name of a person with which the question is associated,“closest” may be identified as a word indicative of proximity orrelationship, and “advisors” may be indicative of a noun or otherlanguage topic.

The identified major features are then used during the questiondecomposition stage 330 to decompose the question into one or morequeries to be applied to the corpora of data/information 345 in order togenerate one or more hypotheses. The queries are generated in any knownor later developed query language, such as the Structure Query Language(SQL), or the like. The queries are applied to one or more databasesstoring information about the electronic texts, documents, articles,websites, and the like, that make up the corpora of data/information345. That is, these various sources themselves, different collections ofsources, and the like, represent a different corpus 347 within thecorpora 345.

There may be different corpora 347 defined for different collections ofdocuments based on various criteria depending upon the particularimplementation. For example, different corpora may be established fordifferent topics, subject matter categories, sources of information, orthe like. As one example, a first corpus is associated with healthcaredocuments while a second corpus is associated with financial documents.Alternatively, one corpus comprises documents published by the U.S.Department of Energy while another corpus comprises IBM Redbooksdocuments. Any collection of content having some similar attribute isconsidered to be a corpus 347 within the corpora 345.

As used herein, a “domain” is a technical, professional, or academicfield having a corresponding corpus or source of information. Forinstance, one domain is a healthcare domain where a corresponding corpusfor the domain includes healthcare documents and another domain is afinancial domain where a corresponding corpus for the financial domainis a collection of financial documents.

The queries are applied to one or more databases storing informationabout the electronic texts, documents, articles, websites, and the like,that make up the corpus of data/information, e.g., the corpus of data106 in FIG. 1. The queries being applied to the corpus ofdata/information at the hypothesis generation stage 340 to generateresults identifying potential hypotheses for answering the inputquestion which can be evaluated. That is, the application of the queriesresults in the extraction of portions of the corpus of data/informationmatching the criteria of the particular query. These portions of thecorpus are then be analyzed and used, during the hypothesis generationstage 340, to generate hypotheses for answering the input question.These hypotheses are also referred to herein as “candidate answers” forthe input question. For any input question, at this stage 340, there maybe hundreds of hypotheses or candidate answers generated that need to beevaluated.

The QA system pipeline 300, in stage 350, then performs a deep analysisand comparison of the language of the input question and the language ofeach hypothesis or “candidate answer” as well as performs evidencescoring to evaluate the likelihood that the particular hypothesis is acorrect answer for the input question. As mentioned above, this mayinvolve using a plurality of reasoning algorithms, each performing aseparate type of analysis of the language of the input question and/orcontent of the corpus that provides evidence in support of, or not, ofthe hypothesis. Each reasoning algorithm generates a score based on theanalysis it performs, which indicates a measure of relevance of theindividual portions of the corpus of data/information extracted byapplication of the queries as well as a measure of the correctness ofthe corresponding hypothesis, i.e., a measure of confidence in thehypothesis.

In the synthesis stage 360, the many relevance scores generated by thevarious reasoning algorithms are synthesized into confidence scores forthe various hypotheses. This process involves applying weights to thevarious scores, where the weights have been determined through trainingof the statistical model employed by the QA system and/or dynamicallyupdated, as described hereafter. The weighted scores are processed inaccordance with a statistical model generated through training of the QAsystem that identifies a manner by which these scores are combined togenerate a confidence score or measure for the individual hypotheses orcandidate answers. This confidence score or measure summarizes the levelof confidence that the QA system has about the evidence that thecandidate answer is inferred by the input question, i.e., that thecandidate answer is the correct answer for the input question.

The resulting confidence scores or measures are processed by a finalconfidence merging and ranking stage 370 which compares the confidencescores and measures, compare them against predetermined thresholds, orperform any other analysis on the confidence scores to determine whichhypotheses/candidate answers are the most likely to be the answer to theinput question. The hypotheses/candidate answers are ranked according tothese comparisons to generate a ranked listing of hypotheses/candidateanswers (hereafter simply referred to as “candidate answers”). From theranked listing of candidate answers, at stage 380, a final answer andconfidence score, or final set of candidate answers and confidencescores, are generated and output to the submitter of the original inputquestion.

After stage 380, or as part of stage 380, the set of candidate answersis output via a graphical user interface, which provides the user withtools for collaborating with the QA system to review, evaluate, andmodify the listing of candidate answers and the evidence associated withthese candidate answers that is evaluated by the QA system. That is, atstage 390, the graphical user interface engine not only receives thefinal ranked listing of candidate answers generated by the QA systempipeline 300, but also receives the underlying evidence information foreach of the candidate answers from the hypothesis and evidence scoringstage 350, and uses this information to generate a graphical userinterface outputting the ranked listing of candidate answers and anoutput of the selected portions of the corpus of data/information thatsupports, and/or detracts, from the candidate answers being the correctanswer for the input question, referred to hereafter as the “evidencepassages.” Stage 390 may also cache candidate answers and evidence in QAcache 395 to more quickly provide answers and supporting evidence forrecently or frequently asked questions.

FIG. 4 is a block diagram illustrating training of a system for ratingdifficulty in accordance with an illustrative embodiment. In thedepicted embodiment, QA system 410 receives a set of trainingquestion/answer pairs with predetermined difficulty scores 401. In anexample embodiment, the question text includes multiple-choice answerselections. In one example embodiment, test training question/answerpairs 401 also includes the question type (e.g., essay, multiple-choice,short answer, etc.). In one example embodiment, an expert, teacher,professor, or other user provides the predetermined difficulty scores.In this example, the user providing the difficulty score is a person whohas thorough knowledge of the subject matter and the source knowledge incorpus 405. In another example embodiment, students or other users of QAsystem 410 provide feedback that the system uses to derive the ultimatedifficulty score. For example, many users may contribute feedback in theform of difficulty scores, which the system then uses to calculate thedifficulty score using a mathematical operation. For instance, thesystem may simply take an average or median of the user-provided scores.In another example, the system may remove outliers before calculatingthe difficulty score for each question/answer pair.

QA system 410 generates candidate answers for the training questions 401as described above with respect to FIGS. 1-3. QA system 410 uses QAsystem pipeline 411, which comprises a plurality of software engines,also referred to as annotation engines or annotators. Each of theannotation engines in QA system pipeline 411 performs a specializedfunction, such as parsing, counting, marking parts of speech, or moresophisticated natural language processing functions, such as identifyingthe topicality/centrality of a topic, identifying discourse structureattributes, etc. The annotation engines in QA system pipeline 411produce output, referred to herein as “features,” to be used by otherannotation engines in the pipeline. These annotation engines processportions of the input question, portions of information in corpus 405,portions of the candidate answers, or features produced by previousannotation engines in the pipeline.

A challenge in computing question difficulty is that “difficulty” in atest question, for example, may arise from a variety of reasons. Forexample, the concept may be unimportant or simply not focused orrepeated in the materials, and, therefore, may be less likely to berecalled by students who skimmed the materials. Only those who read thematerial carefully will recall the fact. As another example, thequestion may be poorly worded or vague; therefore, providing the desiredanswer may be problematic. In addition, the question may require thestudent to synthesize or integrate material or demonstrate mastery ofthe concept by applying the concept in a creative way, rather thansimply recalling what was stated in the materials.

The illustrative embodiments recognize that question difficulty mayarise from various sources and marks differing clues when comparing aquestion to the source pedagogical materials that the question covers.Thus, an illustrative embodiment supplements the annotation engines inQA system pipeline 411 to include annotation engines that producefeatures for generating a question difficulty score. As an example, oneembodiment is to use inverse document frequency as a difficulty measurefor factual questions. In domains dealing with factual questions, thedifficulty of the question is inversely proportional to the probabilitythat the average intended user of the question knows all facts presentwithin the answer. Thus, the more frequently a fact that the questionrelies upon is mentioned in the corpus 405, the more likely the userwill know the fact. Therefore, the difficulty of such a question isinversely proportional to the document frequency. Inverse DocumentFrequency (IDF), which is the inverse of Term Frequency (TF), is auseful measure of question difficulty. If a question contains more thanone identifiable fact, the question difficulty is proportional to the“hardest” fact in the question and increases with the number ofindependent facts in the question.

In addition to corpus-internal measures, QA system pipeline 411 can beused to judge the difficulty of a question by passing the question asinput to QA system 410 and evaluating the output list of ranked/scoredcandidate answers through a plurality of means. For example, if a largeamount of supporting evidence is produced for the highest rankedcandidate answer and there is a large gap between the confidence scoreof the highest ranked candidate answer and the second highest rankedcandidate answer, then the question may be judged to be easy. However,if few candidate answers are found and the amount of supporting evidencedoes not greatly differentiate the top two or three candidate answers,then that question may be assessed as difficult.

Other measures of difficulty are possible and may includedifficulty-related metadata associated with documents, natural language(NL), artificial intelligence (AI), machine learning techniques, etc.Examples of features relevant to question difficulty may include,without limitation:

a number of times the target concept is referenced in the text, eitherthrough a direct mention or via anaphoric mention;

features that indicate the topicality/centrality of the topic when it ismentioned, for example using syntactic clues such as whether the mentionwas in the subject position, intersentential discourse clues, such asthe topical foci determined in the Centering Framework, or discoursestructure attributes, such as whether the phrase containing the conceptis a Nucleus or Satellite as defined in rhetorical structure theory;

tuple frequency: extracting the predicate/argument structure of thequestion as a semantic frame: the frequency of that semantic framewithin the material;

position in the materials of the initial mention of the concept:beginning vs. end of material, a proxy for the complexity of theconcept, assuming that foundational concepts are explained in thematerial early and more complex concepts that build on many others wouldbe discussed later;

whether a definition of the target is contained within the material (thepresence of a definition indicates that the concept is a focus of thematerial and also introduced within the material);

the Term Frequency (TF)/Inverse Document Frequency (IDF) score of theterm within the materials, assuming the materials can be segmented intodocument boundaries; and,

the degree to which the term is used in the sentence in an unexpectedcontext, compared to its typical context within the document set,indicating if the target concept is being used in a way that requiresthe student to recognize a novel application of a familiar concept.

The above list of features is not intended to be limiting. Anycombination of the above features, as well as other features that may bediscovered through natural language processing, artificial intelligence,metadata, machine learning, etc., may contribute to generating aquestion difficulty score.

The illustrative embodiment depicted in FIG. 4 uses supervised machinelearning to train the difficulty score calculation. For each question intest question/answer pairs 401, machine learning component 412identifies the features generated by QA system pipeline 411 that arerelevant to the question and/or contribute to the high confidencecandidate answers that correspond to the known correct answer in thequestion/answer pair. Machine learning component 412 utilizes theidentified set of features to learn a mapping between thequestion/answer material and the difficulty level. Machine learningcomponent 412 assigns weights for the features to scale the questiondifficulty score. In one example embodiment, machine learning component412 uses a linear regression model to calibrate the weights that scaleto the provided difficulty score. The mapping of features and weightscomprise machine learning model 413.

In an alternative embodiment, QA system 410 receives an initial set ofparameters to generate a set of difficulty scores and allows users toprovide feedback. Machine learning component 412 then derives theweights that scale to the difficulty scores generated in this manner.

In yet another example embodiment, machine learning component 412 uses aBayesian classifier, entropy-based model, such as a decision tree oranother appropriate technique known in the art.

In another example embodiment, machine learning component 412 executeswithin a separate data processing system from the QA system 410. Forinstance, QA system 410 and machine learning component 412 may executewithin different servers, different physical machines, or differentvirtual machines. In one embodiment, machine learning component 412executes on a client data processing system that has access to QA system410 via an Application Programming Interface (API) or the like.

FIG. 5 is a block diagram illustrating a system for rating difficulty inaccordance with an illustrative embodiment. QA system 510 receives inputquestions 501. In one embodiment, input questions 501 comprise a set ofquestions generated by a user or system, such as questions generated bya teacher, professor, or expert in the domain, a question papergenerated by QA system 510 or another question generation system,questions from corpus 505, or questions mined from message boards,knowledge bases, online forums, Frequently Asked Questions (FAQ)documents, technical support communications, or the like.

QA system 510, using QA system pipeline 511, then generates candidateanswers using corpus 505 as the knowledge source. QA system pipeline 511generates features 512, including the features described above withrespect to FIG. 4, in the process of generating the candidate answers.

Difficulty score calculator 520 receives features 512 and machinelearning model 502. For each question 521 from input questions 501,difficulty score calculator 520 uses machine learning model 502 togenerate a question difficulty score 522. That is, difficulty scorecalculator 520 applies weights from machine learning model 502 tofeatures 512, as mapped in machine learning model 502. Thus, difficultyscore calculator 520 calculates the difficulty score using the weightsas coefficients to scale the difficulty score 522.

In one embodiment, QA system 510 or difficulty score calculator 520stores question difficulty score 522 in association with question 521—aswell as question type, question answer, and metadata in one exampleembodiment—in corpus 505 or another question storage (not shown). Thus,the stored questions in corpus 505 serves as a source of questions forfuture question sets.

In an example embodiment, a user provides feedback to correct thedifficulty score 522. Difficulty score calculator 520 then adjustsdifficulty score 522 based on user feedback, as well as the machinelearning model 502. Thus, in this embodiment, difficulty scorecalculator 520 includes a machine learning component (not shown) tocontinuously adjust machine learning model 502 as more actual difficultyscores are collected through user feedback.

In one example embodiment, difficulty score calculator 520 is acomponent of QA system 510. However, in another example embodiment,difficulty score calculator 520 executes within a separate dataprocessing system from the QA system 510. For instance, QA system 510and difficulty score calculator 520 may execute within differentservers, different physical machines, or different virtual machines. Inone embodiment, difficulty score calculator 520 executes on a clientdata processing system that has access to QA system 510 via anApplication Programming Interface (API) or the like.

FIG. 6 is a block diagram of a system for selecting a question set inaccordance with an illustrative embodiment. Question set generationsystem 610 receives a policy 601 for generating a question set 602,which may be a test, quiz, or any other set of questions for aparticular topic or source of information. In one embodiment, policy 601is a target difficulty level. In this case, question set generationsystem 610 identifies questions in corpus 605 having a questiondifficulty score that is consistent with the difficulty level specifiedin policy 601.

In one example embodiment, policy 601 also stores a threshold. In thisembodiment, question set generation system 610 identifies questions suchthat each question, or the entire set of questions, has a difficultyscore within the threshold of the specified difficulty level.

In an alternative embodiment, question generation system 610 generatesmultiple sets of questions that have an overall difficulty level that iswithin a threshold of the difficulty level specified in policy 601.Therefore, in this embodiment, question set generation system 610generates a plurality of unique question sets 602 that have anequivalent overall difficulty. This allows a plurality of students toeach take a different test while ensuring all of the tests areequivalent in difficulty.

In other example embodiments, policy 601 may include other parametersnot mentioned for selecting or disregarding questions from corpus 605.For example, policy 601 may include a number of questions for each topicto include in a question set. In another example, policy 601 includes adifficulty level for each topic. Policy 601 may also associatedifficulty levels with corresponding domains or source materials. In yetanother example embodiment, policy 601 associates different difficultylevel for each respective subset of students, such as grade level,Advanced Placement (AP), etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 7 is a flowchart illustrating operation of training a system forrating difficulty in accordance with an illustrative embodiment.Operation begins (block 700), and the system receives a training set ofquestion/answer pairs with difficulty scores (block 701). For eachquestion/answer pair (block 702), the system generates one or morecandidate answers for the question (block 703). The system extractsfeatures of the question/answer pair (block 704) and assigns weights forthe features to scale the difficulty score (block 705).

Then, the system determines whether the question/answer pair is the lastquestion/answer pair (block 706). If the question/answer pair is not thelast question/answer pair, operation returns to block 702 to considerthe next question/answer pair. If the question/answer pair is the lastquestion/answer pair in block 706, the system stores a mapping offeatures to the assigned weights in a machine learning model (block707). Thereafter, operation ends (block 708).

FIG. 8 is a flowchart illustrating operation of a system for ratingdifficulty in accordance with an illustrative embodiment. Operationbegins (block 800), and the system receives an input set of questions(block 801). For each question in the input set (block 802), the systemgenerates one or more candidate answers for the question (block 803).The system extracts features of the question and the one or morecandidate answers (block 804). The system then applies weights from themachine learning model to the extracted features to generate a questiondifficulty score (block 805).

Then, the system determines whether the question is the last question(block 806). If the question is not the last question in the input set,operation returns to block 802 to consider the next question. If thequestion is the last question in block 806, the system stores thequestion in association with the one or more candidate answers and thegenerated difficulty score in the corpus (block 807). In one exampleembodiment, the system generates a difficulty score for the inputquestion set (block 808). Thereafter, operation ends (block 809).

FIG. 9 is a flowchart illustrating operation of a system for selecting aquestion set in accordance with an illustrative embodiment. Operationbegins (block 900), and the system receives a question policy (block901). In an example embodiment, the question policy includes a targetdifficulty level and a threshold. In other embodiments, the questionpolicy may associate different difficulty levels with different domains,source materials, or subsets of test takers. The system generates aquestion set based on the question policy and a corpus of questions andsource material (block 902). Thereafter, operation ends (block 903).

In one example embodiment, the system generates a single question sethaving an overall difficulty score that is within the threshold of thequestion difficulty level in the question policy. In another exampleembodiment, the system generates a question set wherein each question iswithin the threshold of the question difficulty level in the questionpolicy. In another embodiment, the system generates a plurality ofquestion sets having equivalent overall difficulty.

Thus, the illustrative embodiments provide a mechanism for ratingdifficulty of questions given a corpus of knowledge. The illustrativeembodiments provide a training system for training a machine learningmodel to develop a mapping of features associated with questions andanswers to weights for scaling the question difficulty score. Theillustrative embodiments also provide a system for determining aquestion difficulty score for a given input question using the machinelearning model and features extracted from the question and one or morecandidate answers. Furthermore, the illustrative embodiments provide asystem for generating question sets based on a question policy thatindicates a target difficulty level and a threshold.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers. Network adapters mayalso be coupled to the system to enable the data processing system tobecome coupled to other data processing systems or remote printers orstorage devices through intervening private or public networks. Modems,cable modems and Ethernet cards are just a few of the currentlyavailable types of network adapters.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the art. Theembodiment was chosen and described in order to best explain theprinciples of the invention, the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

What is claimed is:
 1. A computer program product comprising a computerreadable storage medium having a computer readable program storedtherein, wherein the computer readable program, when executed on acomputing device, causes the computing device to: receive an inputquestion; generate one or more candidate answers from a corpus ofknowledge using a pipeline of software engines, wherein the pipeline ofsoftware engines generates a plurality of features extracted from thequestion, the one or more candidate answers, or the corpus of knowledge;generate a question difficulty score based on the plurality of featuresusing a machine learning model, wherein the machine learning model mapsfeatures to assigned weights for scaling the difficulty score; store thequestion difficulty score in association with the input question to forma stored set of questions; receive a question policy, wherein thequestion, policy comprises a target difficulty level and a threshold;and select a set of questions from the stored set of questions, whereinthe set of questions have a difficulty within the threshold of thetarget difficulty level.
 2. The computer program product of claim 1,wherein the plurality of features comprise an inverse document frequencymeasure, wherein the inverse document frequency measure is an inverse ofa term frequency measure, wherein the term frequency measure specifies afrequency that a fact or concept appears in the corpus of knowledge. 3.The computer program product of claim 1, wherein the plurality offeatures comprises one or more features from the set consisting of: anumber of times a target concept is referenced in the corpus ofknowledge; a feature indicating a topicality or centrality of a topic;syntactic clues; intersentential discourse clues; discourse structureattributes; a frequency of a predicate/argument structure in the corpusof knowledge; a position in the corpus of knowledge of an initialmention of a concept; whether a definition of a term is contained withinthe corpus of knowledge; or a degree to which a term is used in asentence in an unexpected context compared to a typical context withinthe corpus of knowledge.
 4. The computer program product of claim 1,wherein the computer readable program further causes the computingdevice to train the machine learning model using supervised machinelearning, wherein training the machine learning model comprises:providing a set of training question/answer pairs with associated knowndifficulty scores; generating one or more candidate answers for eachquestion in set of training question/answer pairs from the corpus ofknowledge using the pipeline of software engines, wherein the pipelineof software engines generates a set of features extracted from thequestion, the one or more candidate answers, or the corpus of knowledgefor each question; and assigning a weight for each feature in the set offeatures for each question to map the set of features to the assignedweights for scaling the difficulty score.
 5. An apparatus comprising: aprocessor; and a memory coupled to the processor, wherein the memorycomprises instructions which, when executed by the processor, cause theprocessor to: receive an input question; generate one or more candidateanswers from a corpus of knowledge using a pipeline of software engines,wherein the pipeline of software engines generates a plurality offeatures extracted from the question, the one or more candidate answers,or the corpus of knowledge; generate a question difficulty score basedon the plurality of features using a machine learning model, wherein themachine learning model maps features to assigned weights for scaling thedifficulty score; store the question difficulty score in associationwith the input question to form a stored set of questions; receive aquestion policy, wherein the question policy comprises a targetdifficulty level and a threshold; and select a set of questions from thestored set of questions, wherein the set of questions have a difficultyscore within the threshold of the target difficulty level.
 6. Theapparatus of claim 5, wherein the plurality of features comprise aninverse document frequency measure, wherein the inverse documentfrequency measure is an inverse of a term frequency measure, wherein theterm frequency measure specifies a frequency that a fact or conceptappears in the corpus of knowledge.
 7. The apparatus of claim 5, whereinthe plurality of features comprises one or more features from the setconsisting of: a number of times a target concept is referenced in thecorpus of knowledge; a feature indicating a topicality or centrality ofa topic; syntactic clues; intersentential discourse clues; discoursestructure attributes; a frequency of a predicate/argument structure inthe corpus of knowledge; a position in the corpus of knowledge of aninitial mention of a concept; whether a definition of a term iscontained within the corpus of knowledge; a degree to which a term isused in a sentence in an unexpected context compared to a typicalcontext within the corpus of knowledge.
 8. The apparatus of claim 5,wherein the instructions further cause the processor to train themachine learning model using supervised machine learning, whereintraining the machine learning model comprises: providing a set oftraining question/answer pairs with associated known difficulty scores;generating one or more candidate answers for each question in set oftraining question/answer pairs from the corpus of knowledge using thepipeline of software engines, wherein the pipeline of software enginesgenerates a set of features extracted from the question, the one or morecandidate answers, or the corpus of knowledge for each question; andassigning a weight for each feature in the set of features for eachquestion to map the set of features to the assigned weights for scalingthe difficulty score.
 9. The computer program product of claim 1,wherein generating the one or more candidate answers comprisessubmitting the input question to a question answering system, whereinthe question answering system comprises the pipeline of softwareengines.
 10. The computer program product of claim 9, wherein thepipeline of software engines comprises one or more annotation enginesthat contribute to generation of the one or more candidate answers. 11.The computer program product of claim 1, further comprising training themachine learning model using supervised machine learning.
 12. Thecomputer program product of claim 11, wherein the question policycomprises a target difficulty level and a threshold.
 13. The apparatusof claim 5, wherein generating the one or more candidate answerscomprises submitting the input question to a question answering system,wherein the question answering system comprises the pipeline of softwareengines.
 14. The apparatus of claim 13, wherein the pipeline of softwareengines comprises one or more annotation engines that contribute togeneration of the one or more candidate answers.
 15. The apparatus ofclaim 5, further comprising training the machine learning model usingsupervised machine learning.
 16. The apparatus of claim 15, wherein thequestion policy comprises a target difficulty level and a threshold.